Call for Papers
[DEBUG]The workshop invites submissions on a wide range of topics, including but not limited to:
- Proposing novel knowledge representations that are derived from transdisciplinary research
- Using knowledge graphs or other types of symbolic Knowledge to improve the quality of LLMs
- Exploring the reasoning mechanism of LLMs
- Distilling symbolic knowledge from LLMs
- Proposing benchmark datasets and evaluation matrices for neuro-symbolic approaches to NLP tasks
- Proposing novel NLP tasks for neuro-symbolic approaches
- NLP applications in classification, sense-disambiguation, sentiment analysis, question-answering, knowledge graph reasoning
- Critical analysis of traditional deep learning or LLMs
- Analysing spatial reasoning of LLMs
- Proposing novel neural computing that may reach symbolic-level reasoning
- Proposing benchmark datasets and matrices to evaluate the gap between neural reasoning and symbolic reasoning
- Addressing efficiency issues in neuro-symbolic systems
- Identifying challenges and opportunities of neuro-symbolic systems
- Developing retrieval augmented models for combining KG and LLMs
- Applying neuro-symbolic approaches to humor generation and other real-life applications
Paper types and formats and templates
Same as LREC-Coling 2024 PAPER TYPES AND FORMATS
Paper submissions
Important Dates
All deadlines are 11:59PM UTC-12:00 (“anywhere on Earth”)
Submission Deadline: March 3, 2024
Notification of Acceptance:March 27, 2024April 3, 2024
Submission of final paper (latex version preferred):April 3, 2024April 10, 2024
Camera-Ready papers due: April 8, 2024 April 15, 2024
Workshop Day: May 21, 2024
Note
When submitting a paper from the START page, please provide essential information about resources (in a broad sense, i.e. also technologies, standards, evaluation kits, etc.) that have been used for the work described in the paper or are a new result of your research. Moreover, ELRA encourages all LREC-COLING authors to share the described LRs (data, tools, services, etc.) to enable their reuse and replicability of experiments (including evaluation ones).